{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# DENOISING AUTO-ENCODER"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PACKAGES LOADED\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "import matplotlib.pyplot as plt\n",
    "from tensorflow.examples.tutorials.mnist import input_data\n",
    "%matplotlib inline  \n",
    "print (\"PACKAGES LOADED\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# MNIST"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting data/train-images-idx3-ubyte.gz\n",
      "Extracting data/train-labels-idx1-ubyte.gz\n",
      "Extracting data/t10k-images-idx3-ubyte.gz\n",
      "Extracting data/t10k-labels-idx1-ubyte.gz\n"
     ]
    }
   ],
   "source": [
    "mnist = input_data.read_data_sets('data/', one_hot=True)\n",
    "trainimg   = mnist.train.images\n",
    "trainlabel = mnist.train.labels\n",
    "testimg    = mnist.test.images\n",
    "testlabel  = mnist.test.labels"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# DEVICE TO USE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "device2use = \"/gpu:0\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# DEFINE NETWORK"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Network Parameters\n",
    "n_input    = 784 # MNIST data input (img shape: 28*28)\n",
    "n_hidden_1 = 256 # 1st layer num features\n",
    "n_hidden_2 = 256 # 2nd layer num features\n",
    "n_output   = 784 # \n",
    "with tf.device(device2use):\n",
    "    # tf Graph input\n",
    "    x = tf.placeholder(\"float\", [None, n_input])\n",
    "    y = tf.placeholder(\"float\", [None, n_output])\n",
    "    dropout_keep_prob = tf.placeholder(\"float\")\n",
    "    # Store layers weight & bias\n",
    "    weights = {\n",
    "        'h1': tf.Variable(tf.random_normal([n_input, n_hidden_1])),\n",
    "        'h2': tf.Variable(tf.random_normal([n_hidden_1, n_hidden_2])),\n",
    "        'out': tf.Variable(tf.random_normal([n_hidden_2, n_output]))\n",
    "    }\n",
    "    biases = {\n",
    "        'b1': tf.Variable(tf.random_normal([n_hidden_1])),\n",
    "        'b2': tf.Variable(tf.random_normal([n_hidden_2])),\n",
    "        'out': tf.Variable(tf.random_normal([n_output]))\n",
    "    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NETWORK READY\n"
     ]
    }
   ],
   "source": [
    "with tf.device(device2use):\n",
    "    # Create model\n",
    "    def denoising_autoencoder(_X, _weights, _biases, _keep_prob):\n",
    "        layer_1 = tf.nn.sigmoid(tf.add(tf.matmul(_X, _weights['h1']), _biases['b1'])) \n",
    "        layer_1out = tf.nn.dropout(layer_1, _keep_prob) \n",
    "        layer_2 = tf.nn.sigmoid(tf.add(tf.matmul(layer_1out, _weights['h2']), _biases['b2'])) \n",
    "        layer_2out = tf.nn.dropout(layer_2, _keep_prob) \n",
    "        return tf.nn.sigmoid(tf.matmul(layer_2out, _weights['out']) + _biases['out'])\n",
    "print (\"NETWORK READY\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# DEFINE FUNCTIONS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FUNCTIONS READY\n"
     ]
    }
   ],
   "source": [
    "with tf.device(device2use):\n",
    "    # MODEL\n",
    "    out = denoising_autoencoder(x, weights, biases, dropout_keep_prob)\n",
    "    # DEFINE LOSS AND OPTIMIZER\n",
    "    cost = tf.reduce_mean(tf.pow(out-y, 2))\n",
    "    optimizer = tf.train.AdamOptimizer(learning_rate=0.01).minimize(cost) \n",
    "    # INITIALIZE\n",
    "    init = tf.initialize_all_variables()\n",
    "    # SAVER\n",
    "    savedir = \"nets/\"\n",
    "    saver = tf.train.Saver(max_to_keep=3) \n",
    "print (\"FUNCTIONS READY\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# OPTIMIZE "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "do_train = 1\n",
    "sess = tf.Session(config=tf.ConfigProto(\n",
    "        allow_soft_placement=True, log_device_placement=True))\n",
    "sess.run(init)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "START OPTIMIZATION\n",
      "Epoch: 000/030 cost: 0.107168743\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/lib/pymodules/python2.7/matplotlib/collections.py:548: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if self._edgecolors == 'face':\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4ab3686710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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xcc3SSGJBeyAH7XWl1mtwLsaorLJsV4eUtwSkEVuA1q1bW+WuFFJSMMyGDRus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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4a9c04e950>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4a743e87d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 005/030 cost: 0.033176764\n",
      "Epoch: 010/030 cost: 0.026914674\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4bb9450210>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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RPmvWLLWMlmlJy+YEcui5oHNIWE/AMBJOoSkByztgGDETMQORRpgI361w4f+W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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4a742a8110>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4a7c0e6290>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 015/030 cost: 0.024993547\n",
      "Epoch: 020/030 cost: 0.023792257\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4a7c0ebe10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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zVAkoSsxRJaAoMSfflIBmIFKUiAkRctxFkIQ/bTDRwNdiQgI+EaTi5NDFUVJn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\nTVEURVEURVEURVEURVEURVEUBYD/D3LIfThfNIxJAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4a742b1b90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4a7413f790>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 025/030 cost: 0.023201655\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4ab3730d10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Hn1SByIs1a9ZY5VIFKoBu3bpZ5VJeRoA9e6zr3lIigGHwI8wivHj+Kf4UyKt0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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4a74298790>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4a74080b90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization Finished!\n"
     ]
    }
   ],
   "source": [
    "training_epochs = 30\n",
    "batch_size      = 100\n",
    "display_step    = 5\n",
    "plot_step       = 10\n",
    "if do_train:\n",
    "    print (\"START OPTIMIZATION\")\n",
    "    for epoch in range(training_epochs):\n",
    "        avg_cost = 0.\n",
    "        num_batch = int(mnist.train.num_examples/batch_size)\n",
    "        for i in range(num_batch): \n",
    "            randidx = np.random.randint(trainimg.shape[0], size=batch_size)\n",
    "            batch_xs = trainimg[randidx, :]\n",
    "            batch_xs_noisy = batch_xs + 0.3*np.random.randn(batch_xs.shape[0], 784)\n",
    "            feed1 = {x: batch_xs_noisy, y: batch_xs, dropout_keep_prob: 0.5}\n",
    "            sess.run(optimizer, feed_dict=feed1)\n",
    "            feed2 = {x: batch_xs_noisy, y: batch_xs, dropout_keep_prob: 1}\n",
    "            avg_cost += sess.run(cost, feed_dict=feed2)/num_batch\n",
    "\n",
    "        # DISPLAY\n",
    "        if epoch % display_step == 0:\n",
    "            print (\"Epoch: %03d/%03d cost: %.9f\" % (epoch, training_epochs, avg_cost))\n",
    "        if epoch % plot_step == 0 or epoch == training_epochs-1:\n",
    "            # TEST\n",
    "            randidx  = np.random.randint(testimg.shape[0], size=1)\n",
    "            testvec  = testimg[randidx, :]\n",
    "            noisyvec = testvec + 0.3*np.random.randn(1, 784)\n",
    "            outvec   = sess.run(out, feed_dict={x: testvec, dropout_keep_prob: 1.})\n",
    "            outimg   = np.reshape(outvec, (28, 28))\n",
    "\n",
    "            # PLOT \n",
    "            plt.matshow(np.reshape(testvec, (28, 28)), cmap=plt.get_cmap('gray'))\n",
    "            plt.title(\"[\" + str(epoch) + \"] Original Image\")\n",
    "            plt.colorbar()\n",
    "            plt.matshow(np.reshape(noisyvec, (28, 28)), cmap=plt.get_cmap('gray'))\n",
    "            plt.title(\"[\" + str(epoch) + \"] Input Image\")\n",
    "            plt.colorbar()\n",
    "            plt.matshow(outimg, cmap=plt.get_cmap('gray'))\n",
    "            plt.title(\"[\" + str(epoch) + \"] Reconstructed Image\")\n",
    "            plt.colorbar()\n",
    "            plt.show()\n",
    "\n",
    "            # SAVE\n",
    "            saver.save(sess, savedir + 'dae_dr.ckpt', global_step=epoch)\n",
    "            \n",
    "    print (\"Optimization Finished!\")\n",
    "else:\n",
    "    print (\"RESTORE\")\n",
    "    saver.restore(sess, \"nets/dae_dr.ckpt-\" + str(training_epochs-1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# TEST"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "label is 4\n",
      "Salt and Pepper Noise\n"
     ]
    },
    {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4a7433ddd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4a7c0b4490>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4a6c386710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "randidx   = np.random.randint(testimg.shape[0], size=1)\n",
    "orgvec    = testimg[randidx, :]\n",
    "testvec   = testimg[randidx, :]\n",
    "label     = np.argmax(testlabel[randidx, :], 1)\n",
    "\n",
    "print (\"label is %d\" % (label)) \n",
    "# Noise type\n",
    "ntype = 2 # 1: Gaussian Noise, 2: Salt and Pepper Noise\n",
    "if ntype is 1:\n",
    "    print (\"Gaussian Noise\")\n",
    "    noisyvec = testvec + 0.1*np.random.randn(1, 784)\n",
    "else:    \n",
    "    print (\"Salt and Pepper Noise\")\n",
    "    noisyvec = testvec\n",
    "    rate     = 0.20\n",
    "    noiseidx = np.random.randint(testimg.shape[1], size=int(testimg.shape[1]*rate))\n",
    "    noisyvec[0, noiseidx] = 1-noisyvec[0, noiseidx]\n",
    "\n",
    "outvec   = sess.run(out, feed_dict={x: noisyvec, dropout_keep_prob: 1})\n",
    "outimg   = np.reshape(outvec, (28, 28))\n",
    "\n",
    "# Plot \n",
    "plt.matshow(np.reshape(orgvec, (28, 28)), cmap=plt.get_cmap('gray'))\n",
    "plt.title(\"Original Image\")\n",
    "plt.colorbar()\n",
    "\n",
    "plt.matshow(np.reshape(noisyvec, (28, 28)), cmap=plt.get_cmap('gray'))\n",
    "plt.title(\"Input Image\")\n",
    "plt.colorbar()\n",
    "\n",
    "plt.matshow(outimg, cmap=plt.get_cmap('gray'))\n",
    "plt.title(\"Reconstructed Image\")\n",
    "plt.colorbar()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PLOT FILTER SHAPES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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8NCRKHGxA/O9GPkTZjiy4eh5s6dEg2KT797jtIFzwC+jA53QXIVxGwDvK8Irs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E6aTH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\nsRV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      "text/plain": [
       "<PIL.Image.Image image mode=L size=289x289 at 0x7F4AB36DF290>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Visualize Filter \n",
    "from PIL import Image\n",
    "\n",
    "def scale_to_unit_interval(ndar, eps=1e-8):\n",
    "    \"\"\" Scales all values in the ndarray ndar to be between 0 and 1 \"\"\"\n",
    "    ndar = ndar.copy()\n",
    "    ndar -= ndar.min()\n",
    "    ndar *= 1.0 / (ndar.max() + eps)\n",
    "    return ndar\n",
    "\n",
    "def tile_raster_images(X, img_shape, tile_shape, tile_spacing=(0, 0),\n",
    "                       scale_rows_to_unit_interval=True,\n",
    "                       output_pixel_vals=True):\n",
    "    assert len(img_shape) == 2\n",
    "    assert len(tile_shape) == 2\n",
    "    assert len(tile_spacing) == 2\n",
    "    out_shape = [(ishp + tsp) * tshp - tsp for ishp, tshp, tsp\n",
    "                      in zip(img_shape, tile_shape, tile_spacing)]\n",
    "\n",
    "    if isinstance(X, tuple):\n",
    "        assert len(X) == 4\n",
    "        # Create an output numpy ndarray to store the image\n",
    "        if output_pixel_vals:\n",
    "            out_array = np.zeros((out_shape[0], out_shape[1], 4), dtype='uint8')\n",
    "        else:\n",
    "            out_array = np.zeros((out_shape[0], out_shape[1], 4), dtype=X.dtype)\n",
    "\n",
    "        #colors default to 0, alpha defaults to 1 (opaque)\n",
    "        if output_pixel_vals:\n",
    "            channel_defaults = [0, 0, 0, 255]\n",
    "        else:\n",
    "            channel_defaults = [0., 0., 0., 1.]\n",
    "\n",
    "        for i in range(4):\n",
    "            if X[i] is None:\n",
    "                # if channel is None, fill it with zeros of the correct\n",
    "                # dtype\n",
    "                out_array[:, :, i] = np.zeros(out_shape,\n",
    "                      dtype='uint8' if output_pixel_vals else out_array.dtype\n",
    "                      ) + channel_defaults[i]\n",
    "            else:\n",
    "                # use a recurrent call to compute the channel and store it\n",
    "                # in the output\n",
    "                out_array[:, :, i] = tile_raster_images(X[i], img_shape, tile_shape, tile_spacing, scale_rows_to_unit_interval, output_pixel_vals)\n",
    "        return out_array\n",
    "\n",
    "    else:\n",
    "        # if we are dealing with only one channel\n",
    "        H, W = img_shape\n",
    "        Hs, Ws = tile_spacing\n",
    "\n",
    "        # generate a matrix to store the output\n",
    "        out_array = np.zeros(out_shape, dtype='uint8' if output_pixel_vals else X.dtype)\n",
    "\n",
    "\n",
    "        for tile_row in range(tile_shape[0]):\n",
    "            for tile_col in range(tile_shape[1]):\n",
    "                if tile_row * tile_shape[1] + tile_col < X.shape[0]:\n",
    "                    if scale_rows_to_unit_interval:\n",
    "                        # if we should scale values to be between 0 and 1\n",
    "                        # do this by calling the `scale_to_unit_interval`\n",
    "                        # function\n",
    "                        this_img = scale_to_unit_interval(X[tile_row * tile_shape[1] + tile_col].reshape(img_shape))\n",
    "                    else:\n",
    "                        this_img = X[tile_row * tile_shape[1] + tile_col].reshape(img_shape)\n",
    "                    # add the slice to the corresponding position in the\n",
    "                    # output array\n",
    "                    out_array[\n",
    "                        tile_row * (H+Hs): tile_row * (H + Hs) + H,\n",
    "                        tile_col * (W+Ws): tile_col * (W + Ws) + W\n",
    "                        ] \\\n",
    "                        = this_img * (255 if output_pixel_vals else 1)\n",
    "        return out_array\n",
    "\n",
    "# Visualize filter\n",
    "w1 = sess.run(weights[\"h1\"])\n",
    "\n",
    "image = Image.fromarray(tile_raster_images(\n",
    "        X = w1.T,\n",
    "        img_shape=(28, 28), tile_shape=(10, 10),\n",
    "        tile_spacing=(1, 1)))\n",
    "image"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
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 },
 "nbformat": 4,
 "nbformat_minor": 0
}
